One of the most common questions marketing leaders ask when they first start thinking about AI brand visibility is: "Is our position normal, or is it a problem?"
That's the right question. AI brand monitoring data is only meaningful in context. A 15% mention rate might be exceptional in a crowded SaaS category or unremarkable in a category where a single dominant brand gets 80% of all AI citations.
This guide presents industry-by-industry benchmarks for AI brand visibility, drawing on research from academic studies, platform-specific analyses, and aggregated data from AI monitoring tools. It's designed to help you calibrate where you stand and what "good" looks like in your specific sector.
The Baseline Reality: AI Citation Rates Are Lower Than Most Brands Expect
Before diving into sector-specific data, one finding from recent research should recalibrate expectations across the board.
A 2024 study examining local business recommendations found that only 1.2% of location-specific queries resulted in a specific business being recommended by ChatGPT. Most queries generated generic advice rather than named recommendations.
At the category level, the picture is somewhat better — but not dramatically so. Research tracking brand mention rates across product category queries found:
- US-based brands: Citation rate of approximately 10.31%
- Non-US brands (English-speaking markets): Citation rates of 3.73–6.58%
- Non-US brands (non-English markets): Citation rates below 3.5%
These numbers reflect the genuine scarcity of AI citation real estate. An AI response to "what's the best CRM for startups?" might name 5–8 brands. If there are 200 meaningful players in the CRM category, each brand's baseline mention probability is low — which means any meaningful presence above baseline represents competitive advantage.
Understanding the Benchmark Framework
The benchmarks below are structured around three key metrics:
Mention Rate: The percentage of relevant prompts in a given category that include your brand. This is the primary visibility metric.
Share of Voice: Your brand's share of total brand mentions across all prompts in your category. A brand with a 40% share of voice is mentioned in 40% of all brand mentions, even if mention rate is lower.
Sentiment Score: The average sentiment framing of your mentions. This is scored as positive (recommended), neutral (acknowledged), or negative (caveated or not recommended).
Industry benchmarks are presented as ranges, reflecting variation within categories. The "top quartile" numbers represent what the leading brands in each sector achieve; "median" reflects what a moderately successful AI visibility effort looks like.
SaaS and B2B Software
Category characteristics: High buyer sophistication, research-intensive purchasing decisions, strong presence on review platforms (G2, Capterra), active content marketing community.
AI platform behaviour: ChatGPT and Claude are heavily used for SaaS research. Buyers ask specific use-case questions ("what's the best project management tool for remote teams?") and comparison questions ("Asana vs Monday.com vs ClickUp — what should I choose?"). Perplexity is increasingly used by technical evaluators.
Typical mention rate benchmarks:
- Top quartile: 18–25%
- Median: 8–12%
- Bottom quartile: 2–5%
Key drivers of above-benchmark performance: Strong G2 presence (1,000+ reviews), consistent appearance in comparison articles, active PR in tech media, documented case studies with specific ROI metrics.
Common benchmark killers: Generic positioning ("a platform for business"), stale review profiles, absence from major "best of" roundups, no structured data implementation.
What good looks like: HubSpot, Salesforce, and Notion consistently appear in the top tier for their respective category queries — not because of advertising spend on AI platforms (which doesn't exist as a mechanism) but because they have massive review footprints, extensive press coverage, and are referenced across thousands of comparison articles.
Implication for smaller SaaS brands: You can't outspend your way to ChatGPT visibility. But you can out-authority the competition on specific use cases. Owning a niche — "the best project management tool for architecture firms" rather than fighting for generic "best project management tool" — is a viable path to meaningful AI brand visibility.
E-commerce and Consumer Brands
Category characteristics: High volume of consumer queries, strong price sensitivity, review platforms (Amazon, Trustpilot, Google reviews) heavily weighted, brand sentiment matters more than in B2B.
AI platform behaviour: Consumer purchasing queries on AI assistants tend to be higher-level ("what's a good running shoe for flat feet?") or comparison-based ("compare Nike React and Adidas Ultraboost for trail running"). Product-level citations are less common than brand/range-level citations.
Typical mention rate benchmarks:
- Top quartile: 22–30%
- Median: 10–15%
- Bottom quartile: 3–6%
E-commerce benchmarks are higher than many other sectors because AI models have extensive training data on consumer brands from shopping platforms, review sites, and consumer publications.
Key drivers of above-benchmark performance: Strong Amazon seller presence (for physical goods), high Trustpilot review volume, celebrity/influencer associations in training data, extensive product description content across multiple retail platforms, and brand associations with specific value propositions (durability, sustainability, performance).
Regional variation is significant: US consumer brands show citation rates 2–3x higher than equivalent European brands for English-language queries. This reflects both the US-heavy composition of AI training data and the higher English-language content volume around US brands.
What good looks like: For running shoes, a brand query "what's the best running shoe for marathon training?" will reliably surface Nike, Adidas, Asics, and Hoka — not because they've done anything AI-specific, but because they have decades of training data, massive review volumes, and extensive media coverage.
Implication for challenger e-commerce brands: Competing on generic category queries against established players is very hard. Focus on specific attribute-based queries ("best sustainable running shoes", "best running shoes under $100", "best zero-drop trail shoes") where your positioning is strongest and incumbent brands are weaker.
Financial Services
Category characteristics: Highly regulated industry, AI models are cautious about giving specific financial advice, strong preference for licensed/accredited providers, significant geographic variation.
AI platform behaviour: This sector is unique in that AI models apply notably more caution here than in other categories. ChatGPT frequently includes disclaimers about financial advice and may recommend consulting a qualified advisor rather than naming specific brands. However, for "fintech tool" queries (rather than "financial advice" queries), brand citations are more common.
Typical mention rate benchmarks:
- Top quartile: 12–18% (for fintech tools and platforms)
- Median: 5–8%
- Bottom quartile: 1–3%
Financial services brands overall have lower mention rates, but the quality of mentions when they occur tends to be higher — AI models generally cite financial brands in more confident, recommended terms when they do appear.
Key drivers of above-benchmark performance: Regulatory accreditation and licensing clearly mentioned across all content, strong presence in established financial media (Bloomberg, FT, WSJ), analyst coverage in financial services research, security and compliance documentation that AI models can cite.
What good looks like: For neobanks and fintech platforms, Revolut, Wise, and Monzo consistently appear in the top tier for European markets. For investment platforms, Vanguard, Fidelity, and more recently Robinhood dominate US citations. Their advantage is years of coverage in trusted financial media.
Implication for challenger financial brands: The compliance and trust signals that AI models weight heavily in this sector — regulatory mentions, industry certifications, coverage in established financial media — are genuinely differentiating and genuinely hard to replicate quickly. But they are replicable over 12–18 months with a focused effort.
Healthcare and Wellness
Category characteristics: AI models apply significant caution in healthcare, with systematic inclusion of "consult a healthcare professional" disclaimers. Brand citations for healthcare products and services are lower than most other sectors. However, health technology and wellness tool categories behave more like SaaS.
Typical mention rate benchmarks:
- Healthcare technology/digital health tools: 8–12% top quartile, 3–6% median
- Wellness and fitness products/services: 15–20% top quartile, 6–10% median
- Clinical healthcare providers: Below 5% across the board (AI models almost never recommend specific clinical providers by name)
Key drivers of above-benchmark performance: Strong peer-reviewed research associations, clinical validation documentation publicly available and indexed, coverage in health technology publications (MedCity News, Fierce Healthcare, Healthcare IT News), association with recognised healthcare institutions.
What good looks like: Teladoc, Headspace, and Calm are consistently mentioned in digital health and wellness queries. Their advantage is extensive coverage in both health-focused and mainstream tech media, plus large user bases that generate ongoing review and discussion volume.
Legal Services
Category characteristics: Similar to financial services in AI caution patterns. AI models rarely recommend specific law firms or individual lawyers by name, and are very cautious about specific legal advice. Legal technology tools and self-service legal platforms behave more like SaaS.
Typical mention rate benchmarks:
- Legal technology tools: 10–15% top quartile, 4–7% median
- Traditional law firm services: Under 3% across all tiers
Key drivers of above-benchmark performance: Coverage in legal technology publications (Above the Law, Legaltech News, Law360), bar association affiliations and directory listings, presence in lawyer review platforms (Avvo, Martindale-Hubbell), and for corporate law services, Chambers and Partners rankings.
Agency and Professional Services
Category characteristics: High variation by service type, generally lower mention rates than product companies, AI models struggle to differentiate between agencies, geographic specificity often required.
Typical mention rate benchmarks:
- Marketing and PR agencies: 5–8% top quartile for brand queries, 1–3% median
- Management consulting (large firms): 15–20% top quartile
- Boutique consulting and specialised agencies: Under 5%
The notable exception is the "Big 4" tier of professional services firms — McKinsey, Deloitte, PwC, BCG — which have accumulated massive AI training data footprints from decades of publication and media coverage and regularly appear in the top 20–30% mention rate range.
What These Benchmarks Mean for Your Strategy
A few overarching implications from the industry data:
There is no "good enough" without measurement. These benchmarks are industry-level averages. Your category may be significantly more or less competitive. Knowing your actual mention rate requires tracking, not estimation.
Top quartile performance is achievable. The brands in the top quartile aren't there by accident, but they're also not there through any secret advantage. They have strong review profiles, authoritative press coverage, and consistent positioning. These are all replicable with sustained effort.
Geographic performance is often the biggest gap. Most marketing teams think about AI visibility in their primary market. But if you're a global business, your AI visibility in secondary markets may be dramatically below your primary market performance — and fixing that gap often requires local content, local press coverage, and local review generation rather than simply more English-language activity.
Cross-platform variance is significant. You may be strong on ChatGPT and weak on Perplexity. Or well-cited on Gemini but absent on Claude. Industry benchmarks aggregate across platforms, but your competitive strategy should be platform-specific.
Checking Your Brand's Benchmarks
The most practical use of these industry benchmarks is as a calibration tool: before you invest in AI brand visibility improvement, establish where you currently stand.
Linksii tracks your brand's AI visibility across ChatGPT, Claude, Gemini, and Perplexity — covering your specific category queries, geographic markets, and competitor set. It gives you your actual mention rate, share of voice, and sentiment score benchmarked against what Linksii sees in your industry.
Check your brand's AI visibility score and see how you compare to industry benchmarks.
